To study the acute psychological effects of Coronavirus Disease 2019 (COVID-19) outbreak among healthcare workers (HCWs) in China, a cross-sectional survey was conducted among HCWs during the early period of COVID-19 outbreak. The acute psychological effects including symptoms of depression, anxiety, and post-traumatic stress disorder (PTSD) were assessed using the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder (GAD-7) questionnaire, and the Impact of Event Scale-Revised (IES-R). The prevalence of depression, anxiety, and PTSD was estimated at 15.0%, 27.1%, and 9.8%, respectively. Having an intermediate technical title, working at the frontline, receiving insufficient training for protection, and lacking confidence in protection measures were significantly associated with increased risk for depression and anxiety. Being a nurse, having an intermediate technical title, working at the frontline, and lacking confidence in protection measures were risk factors for PTSD. Meanwhile, not worrying about infection was a protective factor for developing depression, anxiety, and PTSD. Psychological interventions should be implemented among HCWs during the COVID-19 outbreak to reduce acute psychological effects and prevent long-term psychological comorbidities. Meanwhile, HCWs should be well trained and well protected before their frontline exposure.
Data clustering is one of the fundamental techniques in scientific data analysis and data mining. It partitions a data set into groups of similar items, as measured by some distance metric. Over the years, data set sizes have grown rapidly with the exponential growth of computer storage and increasingly automated business and manufacturing processes.Many of these datasets are geographically distributed across multiple sites, e.g. different sales or warehouse locations. To cluster such large and distributed data sets, efficient distributed algorithms are called for to reduce the communication overhead, central storage requirements, and computation time, as well as to bring the resources of multiple machines to bear on a given problem as the data set sizes scale-up. We describe a technique for parallelizing a family of center-based data clustering algorithms. The central idea is to communicate only sufficient statistics, yielding linear speed-up with excellent efficiency. The technique does not involve approximation and may be used orthogonally in conjunction with sampling or aggregation-based methods, such as BIRCH, to lessen the quality degradation of their approximation or to handle larger data sets. We demonstrate in this paper that even for relatively small problem sizes, it can be more cost effective to cluster the data inplace using an exact distributed algorithm than to collect the data in one central location for clustering.
Recently, genetically targeted cancer therapies have been a topic of great interest. Synthetic lethality provides a new approach for the treatment of mutated genes that were previously considered unable to be targeted in traditional genotype-targeted treatments. The increasing researches and applications in the clinical setting made synthetic lethality a promising anticancer treatment option. However, the current understandings on different conditions of synthetic lethality have not been systematically assessed and the application of synthetic lethality in clinical practice still faces many challenges. Here, we propose a novel and systematic classification of synthetic lethality divided into gene level, pathway level, organelle level, and conditional synthetic lethality, according to the degree of specificity into its biological mechanism. Multiple preclinical findings of synthetic lethality in recent years will be reviewed and classified under these different categories. Moreover, synthetic lethality targeted drugs in clinical practice will be briefly discussed. Finally, we will explore the essential implications of this classification as well as its prospects in eliminating existing challenges and the future directions of synthetic lethality.
This paper addresses the adaptive synchronization problem of networked mechanical systems in task space with time-varying communication delays, where both kinematic and dynamic uncertainties are considered and the information flow in the networks is represented by a directed graph. Based on a novel coordination auxiliary system, we first extend existing feedback architecture to achieve synchronization of networked mechanical systems in task space with slow-varying delays. Given that abrupt turns arise for the delays sometimes, we then propose a delay-independent adaptive synchronization control scheme which removes the requirement of the slow-varying condition. Both of the two control schemes are established with time-domain approaches by using Lyapunov-Krasovskii functions. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.
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